In the field of medical imaging, various systems have been developed for generating medical images of various anatomical structures of individuals for the purpose of screening and evaluating medical conditions. These imaging systems include, for example, CT (computed tomography) imaging, MRI (magnetic resonance imaging), X-ray systems, ultrasound systems, PET (positron emission tomography) systems, etc. Each imaging modality may provide unique advantages over other modalities for screening and evaluating certain types of diseases, medical conditions or anatomical abnormalities, including, for example, colonic polyps, aneurisms, lung nodules, calcification on heart or artery tissue, cancer micro calcifications or masses in breast tissue, and various other lesions or abnormalities.
For example, as is well known in the art, CT (computed tomography) imaging systems can be used to obtain a set of cross-sectional images or 2D “slices” of a ROI (region-of-interest) of a patient for purposes of imaging organs and other anatomies. The CT imaging modality is commonly employed for purposes of diagnosing disease because such modality provides a more precise image that illustrates the size, shape, and location of various anatomical structures such as organs, soft tissues, and bones, and also enables a more accurate evaluation of lesions and abnormal anatomical structures such as cancer, polyps, etc.
One conventional method that physicians, clinicians, radiologists, etc, use for detecting, diagnosing or otherwise evaluating medical conditions is to manually review hard-copies (X-ray films, prints, photographs, etc) of medical images that are reconstructed from an acquired image dataset, to discern characteristic features of interest. For example, CT image data that is acquired during a CT examination can be used to produce a set of 2D medical images (X-ray films) that can be viewed to identify potential abnormal anatomical structures or lesions, for example, based upon the skill and knowledge of the reviewing physician, clinician, radiologist, etc. For example, a mammogram procedure may produce medical images that include normal anatomical structures corresponding to breast tissue, but a trained radiologist may be able identify small lesions among these structures that are potentially cancerous. However, a trained radiologist, physician or clinician may misdiagnose a medical condition such as breast cancer due to human error.
Accordingly, various image data processing systems and tools have been developed to assist physicians, clinicians, radiologists, etc, in evaluating medical images to diagnose medical conditions. For example, computer-aided detection/diagnosis tools have been developed for various clinical applications to provide computer-assisted detection/diagnosis of medical conditions in medical images. In general, these CAD systems employ image data processing methods to automatically detect/diagnose possible lesions and other abnormal anatomical structures such as colonic polyps, aneurisms, lung nodules, calcification on heart or artery tissue, micro calcifications or masses in breast tissue, etc. More specifically, conventional CAD tools include methods for analyzing image data to automatically detect regions of features of interest in the image data which are identified as being potential lesions, abnormalities, disease states, etc. When the processed image data is rendered and displayed, the detected regions or features in the displayed image are “marked” or otherwise highlighted to direct the attention of the radiologist to the potential medical conditions.
Although CAD systems can be very useful for diagnostic/decision support assistance, the accuracy of the CAD system will vary depending on the manner in which the CAD process is programmed. In general, CAD systems can be implemented using “expert systems” in which the CAD process is developed and derived from a set of binary logic classification rules dictated by a human expert and translated into code, or trained using knowledge that is otherwise acquired heuristically. Unfortunately, expert systems which use binary logic classification rules or heuristic learning methods for developing the CAD process are inherently subjective to the expert developer and, consequently such systems are prone to errors due to the subjective nature of the design.
Moreover, with these conventional systems, human domain experts must learn and understand the reasons for classification errors and then manually update the classification rules to provide an acceptable level of accuracy. As such, these conventional methods are costly to implement and maintain due to the significant time and expense that is required for human experts to understand/learn the errors and generate/modify the appropriate rules to obtain more accurate detection results.
Furthermore, CAD systems can be implemented using principle (machine) learning classification methods, wherein an “off line” learning process can be used to train/build one or more classifiers for the CAD process using training data that is learned from a large database of previously diagnosed/labeled cases. Although the performance of the classifiers may be adequate when tested with the training data used to build the classifiers, the run-time performance of such classifiers can be poor when deployed in a CAD system when analyzing information that was not included in the original set of learning data.
For the above conventional programming paradigms, the CAD process may provide sub-optimal and generate incorrect results. For instance, the results of a CAD analysis can include “false positives” by incorrectly marking normal regions, or the CAD analysis may result in “unmarked” but nonetheless abnormal regions. In such instances, the physician's reliance on incorrect CAD marks could result in significant/substantial changes in a patient management process due to extra testing or biopsies, time lost by the radiologist, increased healthcare costs, trauma to the patient, and lead to a lack of trust in computer-assisted diagnosis systems.